A Computational Theory of Mindfulness Based Cognitive Therapy from the “Bayesian Brain” Perspective

Mindfulness Based Cognitive Therapy (MBCT) combines cognitive behavioral therapy with meditation to prevent relapse in recurrent depression, but its cognitive mechanisms are poorly understood computationally or biologically. This article proposes a testable theory grounded in Bayesian brain concepts from cognitive neuroscience, such as predictive coding, where the brain models its environment and updates predictions based on error signals. Core MBCT concepts—being mode, decentring, and reactivity—are reinterpreted as perceptual and metacognitive processes relying on specific computational mechanisms. The theory can be tested experimentally with behavioral paradigms, computational modeling, and neuroimaging, potentially refining both conceptual and practical aspects of MBCT.

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Abstract

Mindfulness Based Cognitive Therapy (MBCT) was developed to combine methods from cognitive behavioural therapy and meditative techniques, with the specific goal of preventing relapse in recurrent depression. While supported by empirical evidence from multiple clinical trials, the cognitive mechanisms behind the effectiveness of MBCT are not well understood in computational (information processing) or biological terms.This article introduces a testable theory about the computational mechanisms behind MBCT that is grounded in “Bayesian brain” concepts of perception from cognitive neuroscience, such as predictive coding. These concepts regard the brain as embodying a model of its environment (including the external world and the body) which predicts future sensory inputs and is updated by prediction errors, depending on how precise these error signals are.This article offers a concrete proposal how core concepts of MBCT – the being mode, decentring, and reactivity – could be understood in terms of perceptual and metacognitive processes that draw on specific computational mechanisms of the “Bayesian brain”. Importantly, the proposed theory can be tested experimentally, using a combination of behavioural paradigms, computational modelling, and neuroimaging. The novel theoretical perspective on MBCT described in this paper may offer opportunities for finessing the conceptual and practical aspects of MBCT.